Data Preparation and Preprocessing.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Load and Inspect DataLoad and Inspect Data ",
   "id": "a7e24a348486d981"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-19T13:05:52.578321Z",
     "start_time": "2024-11-19T13:05:52.330264Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load datasets\n",
    "bmkg_df = pd.read_csv('BMKG_dataset.csv')  # Replace with actual file path\n",
    "jma_df = pd.read_csv('JMA_dataset.csv')    # Replace with actual file path\n",
    "\n",
    "# Display the first few rows and check column names for both datasets\n",
    "print(\"BMKG Dataset:\")\n",
    "print(bmkg_df.head())\n",
    "print(bmkg_df.info())\n",
    "\n",
    "print(\"\\nJMA Dataset:\")\n",
    "print(jma_df.head())\n",
    "print(jma_df.info())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMKG Dataset:\n",
      "          tgl            ot   lat     lon  depth  mag  \\\n",
      "0  2008/11/01  21:02:43.058 -9.18  119.06     10  4.9   \n",
      "1  2008/11/01  20:58:50.248 -6.55  129.64     10  4.6   \n",
      "2  2008/11/01  17:43:12.941 -7.01  106.63    121  3.7   \n",
      "3  2008/11/01  16:24:14.755 -3.30  127.85     10  3.2   \n",
      "4  2008/11/01  16:20:37.327 -6.41  129.54     70  4.3   \n",
      "\n",
      "                     remark  strike1  dip1  rake1  strike2  dip2  rake2  \n",
      "0  Sumba Region - Indonesia      NaN   NaN    NaN      NaN   NaN    NaN  \n",
      "1                 Banda Sea      NaN   NaN    NaN      NaN   NaN    NaN  \n",
      "2          Java - Indonesia      NaN   NaN    NaN      NaN   NaN    NaN  \n",
      "3         Seram - Indonesia      NaN   NaN    NaN      NaN   NaN    NaN  \n",
      "4                 Banda Sea      NaN   NaN    NaN      NaN   NaN    NaN  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92887 entries, 0 to 92886\n",
      "Data columns (total 13 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   tgl      92887 non-null  object \n",
      " 1   ot       92887 non-null  object \n",
      " 2   lat      92887 non-null  float64\n",
      " 3   lon      92887 non-null  float64\n",
      " 4   depth    92887 non-null  int64  \n",
      " 5   mag      92887 non-null  float64\n",
      " 6   remark   92887 non-null  object \n",
      " 7   strike1  2735 non-null   float64\n",
      " 8   dip1     2735 non-null   float64\n",
      " 9   rake1    2735 non-null   float64\n",
      " 10  strike2  2735 non-null   float64\n",
      " 11  dip2     2735 non-null   float64\n",
      " 12  rake2    2735 non-null   float64\n",
      "dtypes: float64(9), int64(1), object(3)\n",
      "memory usage: 9.2+ MB\n",
      "None\n",
      "\n",
      "JMA Dataset:\n",
      "         Date      Time    震央地名        Lat        Long  Depth    M 最大震度  \\\n",
      "0  12/31/1985   2:26:48   島根県東部  35°20.3'N  133°12.7'E     12  3.6  震度１   \n",
      "1  12/30/1985  19:11:46    茨城県沖  36°24.4'N  140°41.8'E     55  3.3  震度１   \n",
      "2  12/30/1985  15:56:17   福島県会津  37°12.6'N  139°56.2'E      6  3.5  震度１   \n",
      "3  12/30/1985  15:20:15  奄美大島近海  27°58.1'N  129°39.9'E      0  4.2  震度１   \n",
      "4  12/29/1985   9:22:20     釧路沖  42°53.3'N  145°26.7'E     35  3.7  震度２   \n",
      "\n",
      "  Source.Name  \n",
      "0         NaN  \n",
      "1         NaN  \n",
      "2         NaN  \n",
      "3         NaN  \n",
      "4         NaN  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 93203 entries, 0 to 93202\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Date         93203 non-null  object\n",
      " 1   Time         93203 non-null  object\n",
      " 2   震央地名         93203 non-null  object\n",
      " 3   Lat          93203 non-null  object\n",
      " 4   Long         93203 non-null  object\n",
      " 5   Depth        93203 non-null  int64 \n",
      " 6   M            93203 non-null  object\n",
      " 7   最大震度         93203 non-null  object\n",
      " 8   Source.Name  89371 non-null  object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 6.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Data Cleaning",
   "id": "12dc9a204e472855"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:14.563006Z",
     "start_time": "2024-11-19T12:59:14.487469Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Check for missing values\n",
    "print(\"BMKG Missing Values:\\n\", bmkg_df.isnull().sum())\n",
    "print(\"\\nJMA Missing Values:\\n\", jma_df.isnull().sum())\n",
    "\n",
    "# Drop or fill missing values if needed\n",
    "# Example: fill missing values in JMA dataset with median values for numerical columns\n",
    "jma_df.fillna(jma_df.median(), inplace=True)\n",
    "bmkg_df.fillna(bmkg_df.median(), inplace=True)  "
   ],
   "id": "a90a34b9640015d4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMKG Missing Values:\n",
      " tgl            0\n",
      "ot             0\n",
      "lat            0\n",
      "lon            0\n",
      "depth          0\n",
      "mag            0\n",
      "remark         0\n",
      "strike1    90152\n",
      "dip1       90152\n",
      "rake1      90152\n",
      "strike2    90152\n",
      "dip2       90152\n",
      "rake2      90152\n",
      "dtype: int64\n",
      "\n",
      "JMA Missing Values:\n",
      " Date              0\n",
      "Time              0\n",
      "震央地名              0\n",
      "Lat               0\n",
      "Long              0\n",
      "Depth             0\n",
      "M                 0\n",
      "最大震度              0\n",
      "Source.Name    3832\n",
      "dtype: int64\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Date-Time Formatting",
   "id": "726245886dae8742"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:14.887719Z",
     "start_time": "2024-11-19T12:59:14.836864Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#change the column names\n",
    "# Merge Date and Time columns into a single Datetime column\n",
    "bmkg_df['Datetime'] = pd.to_datetime(bmkg_df['tgl'] + ' ' + bmkg_df['ot'])\n",
    "\n",
    "# Drop the original Date and Time columns\n",
    "bmkg_df.drop(columns=['tgl', 'ot'], inplace=True)\n",
    "bmkg_df.drop(columns=['strike1', 'strike2', 'dip1', 'dip2' ,'rake1', 'rake2'], inplace=True)\n",
    "# Display the first few rows of the updated dataset\n",
    "print(\"BMKG Dataset:\")\n",
    "print(bmkg_df.head())\n",
    "print(bmkg_df.info())\n"
   ],
   "id": "815a6a4fd094595e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMKG Dataset:\n",
      "    lat     lon  depth  mag                    remark                Datetime\n",
      "0 -9.18  119.06     10  4.9  Sumba Region - Indonesia 2008-11-01 21:02:43.058\n",
      "1 -6.55  129.64     10  4.6                 Banda Sea 2008-11-01 20:58:50.248\n",
      "2 -7.01  106.63    121  3.7          Java - Indonesia 2008-11-01 17:43:12.941\n",
      "3 -3.30  127.85     10  3.2         Seram - Indonesia 2008-11-01 16:24:14.755\n",
      "4 -6.41  129.54     70  4.3                 Banda Sea 2008-11-01 16:20:37.327\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 92887 entries, 0 to 92886\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   lat       92887 non-null  float64       \n",
      " 1   lon       92887 non-null  float64       \n",
      " 2   depth     92887 non-null  int64         \n",
      " 3   mag       92887 non-null  float64       \n",
      " 4   remark    92887 non-null  object        \n",
      " 5   Datetime  92887 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(1)\n",
      "memory usage: 4.3+ MB\n",
      "None\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:15.461872Z",
     "start_time": "2024-11-19T12:59:15.455697Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#drop the unnecessary columns\n",
    "bmkg_df.drop(columns=['remark'], inplace=True) "
   ],
   "id": "edf09621982d2a1",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Standardize Column Names",
   "id": "380015e54359a93f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:16.236728Z",
     "start_time": "2024-11-19T12:59:16.228708Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#change the column names\n",
    "bmkg_df.rename(columns={'mag':'Magnitude', 'depth':'Depth', 'lat':'Latitude', 'lon':'Longitude'}, inplace=True)\n",
    "\n",
    "# Display the first few rows of the updated dataset\n",
    "print(\"BMKG Dataset:\")\n",
    "print(bmkg_df.head())"
   ],
   "id": "9f55c656287b68ff",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMKG Dataset:\n",
      "   Latitude  Longitude  Depth  Magnitude                Datetime\n",
      "0     -9.18     119.06     10        4.9 2008-11-01 21:02:43.058\n",
      "1     -6.55     129.64     10        4.6 2008-11-01 20:58:50.248\n",
      "2     -7.01     106.63    121        3.7 2008-11-01 17:43:12.941\n",
      "3     -3.30     127.85     10        3.2 2008-11-01 16:24:14.755\n",
      "4     -6.41     129.54     70        4.3 2008-11-01 16:20:37.327\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:21.380946Z",
     "start_time": "2024-11-19T12:59:16.674306Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#change the column names\n",
    "# Merge Date and Time columns into a single Datetime column\n",
    "jma_df['Datetime'] = pd.to_datetime(jma_df['Date'] + ' ' + jma_df['Time'])\n",
    "\n",
    "# Drop the original Date and Time columns\n",
    "jma_df.drop(columns=['Date', 'Time', 'Source.Name', '最大震度','震央地名'], inplace=True)\n",
    "\n",
    "# Display the first few rows of the updated dataset\n",
    "print(\"\\nJMA Dataset:\")\n",
    "print(jma_df.head())"
   ],
   "id": "49cdf07450361a47",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "JMA Dataset:\n",
      "         Lat        Long  Depth    M            Datetime\n",
      "0  35°20.3'N  133°12.7'E     12  3.6 1985-12-31 02:26:48\n",
      "1  36°24.4'N  140°41.8'E     55  3.3 1985-12-30 19:11:46\n",
      "2  37°12.6'N  139°56.2'E      6  3.5 1985-12-30 15:56:17\n",
      "3  27°58.1'N  129°39.9'E      0  4.2 1985-12-30 15:20:15\n",
      "4  42°53.3'N  145°26.7'E     35  3.7 1985-12-29 09:22:20\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:21.488899Z",
     "start_time": "2024-11-19T12:59:21.468340Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#change the column names\n",
    "jma_df.rename(columns={'Lat':'Latitude', 'Long':'Longitude', 'Depth':'Depth', 'M':'Magnitude'}, inplace=True)\n",
    "\n",
    "# Display the first few rows of the updated dataset\n",
    "print(\"\\nJMA Dataset:\")\n",
    "print(jma_df.head())"
   ],
   "id": "ccde8fde14467b87",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "JMA Dataset:\n",
      "    Latitude   Longitude  Depth Magnitude            Datetime\n",
      "0  35°20.3'N  133°12.7'E     12       3.6 1985-12-31 02:26:48\n",
      "1  36°24.4'N  140°41.8'E     55       3.3 1985-12-30 19:11:46\n",
      "2  37°12.6'N  139°56.2'E      6       3.5 1985-12-30 15:56:17\n",
      "3  27°58.1'N  129°39.9'E      0       4.2 1985-12-30 15:20:15\n",
      "4  42°53.3'N  145°26.7'E     35       3.7 1985-12-29 09:22:20\n"
     ]
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Convert Coordinates to Decimal Degrees ",
   "id": "dc6e6b1601bb17a6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:23.613589Z",
     "start_time": "2024-11-19T12:59:23.242297Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "# Updated function to handle both DMS and decimal formats\n",
    "def dms_to_decimal(coord):\n",
    "    # Check if the value is NaN\n",
    "    if pd.isna(coord):\n",
    "        return np.nan\n",
    "    \n",
    "    # If the coordinate is already in decimal format, return it as a float\n",
    "    try:\n",
    "        # Try converting directly to float; if it works, we have a decimal value\n",
    "        return float(coord)\n",
    "    except ValueError:\n",
    "        # If direct conversion fails, it's likely in DMS format\n",
    "        pass\n",
    "    \n",
    "    # For DMS format, extract degrees, minutes, and direction\n",
    "    match = re.match(r\"(\\d+)°(\\d+\\.\\d+)'([NSEW])\", str(coord))\n",
    "    if match:\n",
    "        degrees = float(match.group(1))\n",
    "        minutes = float(match.group(2))\n",
    "        direction = match.group(3)\n",
    "        \n",
    "        # Convert DMS to decimal degrees\n",
    "        decimal = degrees + (minutes / 60)\n",
    "        \n",
    "        # Make coordinates negative for South and West\n",
    "        if direction in ['S', 'W']:\n",
    "            decimal = -decimal\n",
    "            \n",
    "        return decimal\n",
    "    else:\n",
    "        raise ValueError(f\"Invalid coordinate format: {coord}\")\n",
    "\n",
    "# Replace '不明' with NaN in the dataset\n",
    "jma_df.replace('不明', np.nan, inplace=True)\n",
    "\n",
    "# Apply the function to the Latitude and Longitude columns\n",
    "jma_df['Latitude'] = jma_df['Latitude'].apply(dms_to_decimal)\n",
    "jma_df['Longitude'] = jma_df['Longitude'].apply(dms_to_decimal)\n",
    "\n",
    "# Display the updated dataset\n",
    "print(\"\\nUpdated JMA Dataset with Decimal Coordinates:\")\n",
    "print(jma_df.head())\n"
   ],
   "id": "ab72c3b49e05391e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Updated JMA Dataset with Decimal Coordinates:\n",
      "    Latitude   Longitude  Depth Magnitude            Datetime\n",
      "0  35.338333  133.211667     12       3.6 1985-12-31 02:26:48\n",
      "1  36.406667  140.696667     55       3.3 1985-12-30 19:11:46\n",
      "2  37.210000  139.936667      6       3.5 1985-12-30 15:56:17\n",
      "3  27.968333  129.665000      0       4.2 1985-12-30 15:20:15\n",
      "4  42.888333  145.445000     35       3.7 1985-12-29 09:22:20\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Normalization and Scaling",
   "id": "e0628beb194e190f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:28.195111Z",
     "start_time": "2024-11-19T12:59:28.105316Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "# Replace non-numeric values (like '不明') with NaN in the dataset\n",
    "jma_df.replace('不明', np.nan, inplace=True)\n",
    "\n",
    "# Check if there are still non-numeric values in Latitude and Longitude columns\n",
    "# This will help us identify and drop or handle rows with invalid data\n",
    "try:\n",
    "    # Convert the Latitude and Longitude columns to decimal format\n",
    "    jma_df['Latitude'] = jma_df['Latitude'].apply(dms_to_decimal)\n",
    "    jma_df['Longitude'] = jma_df['Longitude'].apply(dms_to_decimal)\n",
    "except ValueError as e:\n",
    "    print(f\"Error converting coordinates: {e}\")\n",
    "\n",
    "# Display any rows with NaN values after replacing '不明'\n",
    "print(\"Rows with NaN values after replacement:\")\n",
    "print(jma_df[jma_df.isna().any(axis=1)])\n",
    "\n",
    "# Option 1: Drop rows with NaN values (if they are few and not critical)\n",
    "jma_df.dropna(inplace=True)\n",
    "\n",
    "# Option 2: Alternatively, you could impute or handle NaN values in a way that makes sense for your model\n",
    "\n",
    "# Display the cleaned dataset\n",
    "print(\"\\nCleaned JMA Dataset:\")\n",
    "print(jma_df.head())"
   ],
   "id": "5dc245285067899e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with NaN values after replacement:\n",
      "        Latitude   Longitude  Depth Magnitude            Datetime\n",
      "6      27.083333  142.200000      0       NaN 1985-12-27 23:36:00\n",
      "9      27.083333  142.200000      0       NaN 1985-12-25 00:22:00\n",
      "22     24.383333  123.750000      0       NaN 1985-12-19 20:27:00\n",
      "26     27.083333  142.200000      0       NaN 1985-12-16 14:37:00\n",
      "36     34.233333  135.166667      0       NaN 1985-12-09 19:20:00\n",
      "...          ...         ...    ...       ...                 ...\n",
      "92324  27.003333  142.423333     67       NaN 2022-04-11 23:01:26\n",
      "92436  27.051667  142.331667     74       NaN 2022-05-23 20:25:37\n",
      "92551  24.561667  141.181667    148       NaN 2022-05-01 14:43:06\n",
      "92813  24.806667  142.643333     67       NaN 2022-07-14 02:49:46\n",
      "92848  26.536667  142.521667     47       NaN 2022-07-06 10:40:15\n",
      "\n",
      "[7869 rows x 5 columns]\n",
      "\n",
      "Cleaned JMA Dataset:\n",
      "    Latitude   Longitude  Depth Magnitude            Datetime\n",
      "0  35.338333  133.211667     12       3.6 1985-12-31 02:26:48\n",
      "1  36.406667  140.696667     55       3.3 1985-12-30 19:11:46\n",
      "2  37.210000  139.936667      6       3.5 1985-12-30 15:56:17\n",
      "3  27.968333  129.665000      0       4.2 1985-12-30 15:20:15\n",
      "4  42.888333  145.445000     35       3.7 1985-12-29 09:22:20\n"
     ]
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# handling the missing values",
   "id": "9050a40e4b327d32"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:29.476805Z",
     "start_time": "2024-11-19T12:59:29.452622Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Interpolate missing values in the 'Magnitude' column\n",
    "jma_df['Magnitude'] = jma_df['Magnitude'].interpolate(method='linear')\n",
    "\n",
    "# Verify that there are no more NaN values in 'Magnitude'\n",
    "print(\"Rows with NaN values in Magnitude after interpolation:\")\n",
    "print(jma_df[jma_df['Magnitude'].isna()])\n"
   ],
   "id": "5d080610cecf7b19",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with NaN values in Magnitude after interpolation:\n",
      "Empty DataFrame\n",
      "Columns: [Latitude, Longitude, Depth, Magnitude, Datetime]\n",
      "Index: []\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:30.169538Z",
     "start_time": "2024-11-19T12:59:30.052221Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Apply scaling to Depth and Magnitude columns in both datasets\n",
    "bmkg_df[['Depth', 'Magnitude']] = scaler.fit_transform(bmkg_df[['Depth', 'Magnitude']])\n",
    "jma_df[['Depth', 'Magnitude']] = scaler.transform(jma_df[['Depth', 'Magnitude']])\n",
    "\n",
    "print(\"\\nScaled BMKG Dataset:\")\n",
    "print(bmkg_df.head())\n",
    "\n",
    "print(\"\\nScaled JMA Dataset:\")\n",
    "print(jma_df.head())\n"
   ],
   "id": "84c59fc5ac8e152b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Scaled BMKG Dataset:\n",
      "   Latitude  Longitude     Depth  Magnitude                Datetime\n",
      "0     -9.18     119.06  0.010695   0.565217 2008-11-01 21:02:43.058\n",
      "1     -6.55     129.64  0.010695   0.521739 2008-11-01 20:58:50.248\n",
      "2     -7.01     106.63  0.159091   0.391304 2008-11-01 17:43:12.941\n",
      "3     -3.30     127.85  0.010695   0.318841 2008-11-01 16:24:14.755\n",
      "4     -6.41     129.54  0.090909   0.478261 2008-11-01 16:20:37.327\n",
      "\n",
      "Scaled JMA Dataset:\n",
      "    Latitude   Longitude     Depth  Magnitude            Datetime\n",
      "0  35.338333  133.211667  0.013369   0.376812 1985-12-31 02:26:48\n",
      "1  36.406667  140.696667  0.070856   0.333333 1985-12-30 19:11:46\n",
      "2  37.210000  139.936667  0.005348   0.362319 1985-12-30 15:56:17\n",
      "3  27.968333  129.665000 -0.002674   0.463768 1985-12-30 15:20:15\n",
      "4  42.888333  145.445000  0.044118   0.391304 1985-12-29 09:22:20\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Train-Test Split",
   "id": "abdbcd53ce39a1a3"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T12:59:31.458138Z",
     "start_time": "2024-11-19T12:59:31.395329Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Define features and target for splitting\n",
    "X_bmkg = bmkg_df[['Latitude', 'Longitude', 'Depth', 'Magnitude']]\n",
    "y_bmkg = bmkg_df['Magnitude']\n",
    "\n",
    "X_jma = jma_df[['Latitude', 'Longitude', 'Depth', 'Magnitude']]\n",
    "y_jma = jma_df['Magnitude']\n",
    "\n",
    "# Split the datasets\n",
    "X_train_bmkg, X_test_bmkg, y_train_bmkg, y_test_bmkg = train_test_split(X_bmkg, y_bmkg, test_size=0.2, random_state=42)\n",
    "X_train_jma, X_test_jma, y_train_jma, y_test_jma = train_test_split(X_jma, y_jma, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"BMKG Train/Test Split:\")\n",
    "print(X_train_bmkg.shape, X_test_bmkg.shape)\n",
    "print(\"\\nJMA Train/Test Split:\")\n",
    "print(X_train_jma.shape, X_test_jma.shape)"
   ],
   "id": "f6124ba88b4f35e8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BMKG Train/Test Split:\n",
      "(74309, 4) (18578, 4)\n",
      "\n",
      "JMA Train/Test Split:\n",
      "(68267, 4) (17067, 4)\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T13:00:30.543519Z",
     "start_time": "2024-11-19T13:00:29.812098Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Save the cleaned and preprocessed datasets\n",
    "bmkg_df.to_csv('BMKG_cleaned.csv', index=False)\n",
    "jma_df.to_csv('JMA_cleaned.csv', index=False)\n",
    "\n",
    "print(\"Datasets saved successfully!\")"
   ],
   "id": "100885a24ce5fcdb",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets saved successfully!\n"
     ]
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T13:00:31.423980Z",
     "start_time": "2024-11-19T13:00:30.620879Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# save the train and test datasets\n",
    "X_train_bmkg.to_csv('X_train_bmkg.csv', index=False)\n",
    "y_train_bmkg.to_csv('y_train_bmkg.csv', index=False)\n",
    "X_test_bmkg.to_csv('X_test_bmkg.csv', index=False)\n",
    "y_test_bmkg.to_csv('y_test_bmkg.csv', index=False)\n",
    "\n",
    "X_train_jma.to_csv('X_train_jma.csv', index=False)\n",
    "y_train_jma.to_csv('y_train_jma.csv', index=False)\n",
    "X_test_jma.to_csv('X_test_jma.csv', index=False)\n",
    "y_test_jma.to_csv('y_test_jma.csv', index=False)\n",
    "\n",
    "print(\"Train and Test datasets saved successfully!\")"
   ],
   "id": "4c581e03de04731b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train and Test datasets saved successfully!\n"
     ]
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T13:00:31.937872Z",
     "start_time": "2024-11-19T13:00:31.456498Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# visualize the data for bmkg dataset\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "# Plot the distribution of Magnitude values in the BMKG dataset\n",
    "plt.figure(figsize=(10, 6))\n",
    "seaborn.histplot(bmkg_df['Magnitude'], bins=20, color='skyblue', kde=True)\n",
    "plt.title('Distribution of Earthquake Magnitudes (BMKG)')\n",
    "plt.xlabel('Magnitude')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ],
   "id": "ddb37ba17ee4caec",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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0M6mZLTCzaPh8FUEHhs3uvhsYNLPzw/vdrgR+EG52K3BV+Pxy4I7wvjkRERGROa+cQ4zcANwNnGRmO8zsHeGiKzi4Q8OFwMNm9hBBJ4V3uft4rdq7ga8CG4FNwO1h+deATjPbSNAE+6FyvRYRERGRalO25lR3f9Mhyt82SdktBEOOTLb+WuD0ScrHgNcfXZQiIiIitUkzNoiIiIjUICVxIiIiIjVISZyIiIhIDVISJyIiIlKDlMSJiIiI1CAlcSIiIiI1SEmciIiISA1SEiciIiJSg5TEiYiIiNQgJXEiIiIiNUhJnIiIiEgNUhInIiIiUoOUxImIiIjUICVxIiIiIjVISZyIiIhIDVISJyIiIlKDlMSJiIiI1CAlcSIiIiI1SEmciIiISA1SEiciIiJSg5TEiYiIiNQgJXEiIiIiNUhJnIiIiEgNUhInIiIiUoOUxImIiIjUICVxIiIiIjVISZyIiIhIDVISJyIiIlKDlMSJiIiI1CAlcSIiIiI1SEmciIiISA1SEiciIiJSg5TEiYiIiNQgJXEidcrdKbhXOgwRETlCsUoHICKzayhbYP9Ynu6xPGN5Jx6BVDRCZyrCosYYEbNKhygiIiVQEidSJ/LuPDWYZc9IHgPaEhEWpKKkC85IrsBTgzl2DedZ1hxjYUO00uGKiMgUytacamZfN7N9Zra+qOwTZrbTzB4MHy8rWvZhM9toZk+Y2UuLys8xs3Xhss+bBdUEZpY0s2+H5feY2YpyvRaRWjeULfDQ/jR7RvIsboxy3sIUp3UkWdYSZ3VbgrM6U5w2L0EiChsHsmzozxJLJCsdtoiIHEY574m7Frh0kvLPuvvZ4eM2ADM7FbgCOC3c5otmNl4V8CXgamB1+Bjf5zuAXnc/Afgs8KlyvRCRWrZ/NMe67jR5d06bl2Bla4JY5OAm0/ZklDM7kixrjtE1ludPv/J9hrKFCkQsIiKlKFsS5+53Aj0lrv4q4EZ3T7v7FmAjcJ6ZLQJa3f1ud3fgOuDVRdt8I3x+M3DJeC2diAQdF7YNZXmiP0tTPMJZnSnak4dvJjUzjmuOc3J7gmOOP4VvbehnWImciEhVqkTv1PeZ2cNhc+u8sGwJsL1onR1h2ZLw+cTyA7Zx9xzQD3ROdkAzu9rM1prZ2q6urpl7JSJVKu/Ok/1Ztg/lWJCKcnpHgkS09O84nako//neNzCYzfPtTf2M5ZTIiYhUm9lO4r4EHA+cDewGPh2WT3Z18cOUH26bgwvdr3H3Ne6+ZsGCBdMKWKTWZPLO+p40+8fyLG+OsbotfkQ9Trc+dC+vXdlK91iemzYNkC1oOBIRkWoyq0mcu+9197y7F4CvAOeFi3YAxxWtuhTYFZYvnaT8gG3MLAa0UXrzrcic1JfO82D3GCM55+T2BEub4xzNXQYrWxO8ckULu0Zy/Hj7EK5x5UREqsasJnHhPW7jXgOM91y9Fbgi7HG6kqADw73uvhsYNLPzw/vdrgR+ULTNVeHzy4E7XFcYqVPuzrbBLI/0ZoiZcWZHks7UzAwTclJ7kucf28j6njQPdo/NyD5FROTolW2cODO7AbgImG9mO4CPAxeZ2dkEzZ5PAe8EcPdHzOwm4FEgB7zX3fPhrt5N0NO1Abg9fAB8DbjezDYS1MBdUa7XIlLNcgXnyf4MvekCC1NRVrXGiU7S+/RoXHBsA7tHsvx0xzDHNMRY3BSf0f2LiMj0Wb1VXq1Zs8bXrl1b6TBEjoqZceeuIcZyBR7tyzCac1a1xlnUOHPfyy5c3HxA8+lorsC1T/QB8I6T502ro4SIiBwZM7vf3ddMtkxzp4rUqLFcgYd70mTzwfhvM5nATaYhFuEVy1vozxT45a7hsh5LRESmpiROpAY1ts3j0d4MBYfTO5JTjv82U45rjrNmQYoH9o/x1GBmVo4pIiKTUxInUmOyBeeqz32TsbxzyrwETfHZ/TN+4eIm5iUj3L5tiEy+vm7HEBGpJkriRGrML3YOs/T0czipPUFbYvYnqo9HjJctC5pVf7NnZNaPLyIiASVxIjVk+1CWB/aPcfeNX5mxIUSOxHHNcc7oSHJf1yjdY7mKxSEiUs+UxInUiGzBuW3bIG2JCD/+wj9UOhwuWtxE3Iyf7RjWIMAiIhWgJE6kRty1e4TedIHLljWTHat8M2ZTPMLzFzWyZTDLhn51chARmW1K4kRqQG86z337RjmzM8mKlkSlw3nasxekmJ+K8vOdw5pbVURklimJE6kBv949QtTgwkVNlQ7lAFEzXry0if5MgXv2jlY6HBGRuqIkTqTK7R/N8UhvmnMWNNA8y8OJlGJFS4KT2xP8bu8I/Zn81BuIiMiMqL4rgogc4K49IyQixnOOaah0KId08ZImzOCOnZrJQURktiiJE6lie0dyPNGXYc3CFI2x6v1zbUtEee4xjTzRl+GpAXVyEBGZDdV7VRARfrd3hGTEOG9B9dbCjTtvYQNtiQg/3zlMQUOOiIiUnZI4kSrVn8nzeF+Gs+anSFVxLdy4WMR40ZImusbyPLh/rNLhiIjMedV/ZRCpUw90BYnQOQtSFY6kdCe2JTiuOcZdu0cYyxUqHY6IyJymJE6kCmXyzoPdYxWbH/VImRkvXtLMaN41r6qISJkpiROpQut6xkjnnXMXVv+9cBMd0xjjrM4k93eNaV5VEZEyUhInUmXcnbVdoyxujLGkKV7pcI7IhYuaiEVMQ46IiJSRkjiRKvPUYJbedKGm7oWbqCke4YJjG9g0kGWzhhwRESkLJXEiVebB7jEaosZJ7clKh3JUzlnQQHsiwh0ackREpCyUxIlUkeFsgQ19Gc7oTBGLWKXDOSrjQ47sH8vzew05IiIy45TEiVSRdT1jFICzOmu7Fm7c6rYEy5vj3LV7hFENOSIiMqOUxIlUCXfnwf1jHNccozMVq3Q4M8LMuGRpE+m882sNOSIiMqOUxIlUia2DWfoyBc7urN0ODZNZ2BDjrM4UD3SNsV9DjoiIzBglcSJV4qHuMVJzoEPDZC5c1EgiYtyxQ0OOiIjMFCVxIlVgLFfgyf4Mp85L1nyHhsk0xiM879gGNg9meUpDjoiIzAglcSJV4PG+DHmHMzrmXi3cuHMWNNCaiPCLXcN4OOTIsuUrMLOyP5YtX1HZFy8iUgZz4+5pkRq3vmeMzlSUYxvn7p9kLGJcuKiR/946xGN9Qa3j9m1buXPXUNmPfeHi5rIfQ0RktqkmTqTCetN5dgznOKMjidnca0otduq8JAtSUe7cNUy+oAGARUSOhpI4kQpb1zOGAafNm7tNqeMiZly0uIm+TIEHuzUAsIjI0VASJ1JB7s76njQrWuK0JKKVDmdWrGqNs6w5zm/2jJBsUjOniMiRUhInUkE7hnMMZAqcPoc7NExkZly8uJGRnPOCt76n0uGIiNSsuXsXtUgNeLQ3TTwCq9vqJ4kDWNQU5+T2BOm3vIdM3klE5/a9gCIi5aCaOJEKyRecx3rTrG5L1mUSc+GiJmLxBNuHs5UORUSkJimJE6mQLYNZxvLOqXXQoWEyHako937vevaO5BnNFSodjohIzSlbEmdmXzezfWa2vqjsn83scTN72My+Z2btYfkKMxs1swfDx5eLtjnHzNaZ2UYz+7yFYzCYWdLMvh2W32NmK8r1WkTK4dHeNKmosbIlXulQKuaOr3waM9g2pDlVRUSmq5w1cdcCl04o+ylwurufCTwJfLho2SZ3Pzt8vKuo/EvA1cDq8DG+z3cAve5+AvBZ4FMz/xJEyiOTdzb0pzm5PUl0Dk6zVaqh7n0sboyxfyzPcFa1cSIi01G2JM7d7wR6JpT9xN3Hv3L/Dlh6uH2Y2SKg1d3v9mCenuuAV4eLXwV8I3x+M3CJzfWRUmXO2NifIVugbptSiy1pihE12Dake+NERKajkvfE/TFwe9HvK83s92b2KzN7QVi2BNhRtM6OsGx82XaAMDHsBzrLG7LIzHi0N01LPMJxzeogHosYS5pi9KQLDGZUGyciUqqKJHFm9hEgB3wzLNoNLHP3ZwEfAL5lZq3AZDVr43P1HG7ZxONdbWZrzWxtV1fX0QUvcpTG8gW2DGY4qT0x56fZKtXixhjxCGwdyhJUuouIyFRmPYkzs6uAVwBvDptIcfe0u3eHz+8HNgEnEtS8FTe5LgV2hc93AMeF+4wBbUxovh3n7te4+xp3X7NgwYKZf1Ei07CxP0Pe4eR2NaWOi0aMpU1x+jMF+lUbJyJSkllN4szsUuCDwCvdfaSofIGZRcPnqwg6MGx2993AoJmdH97vdiXwg3CzW4GrwueXA3e4vsJLDXi8L0NLPMKSJjWlFju2MUoyYqqNExEpUTmHGLkBuBs4ycx2mNk7gC8ALcBPJwwlciHwsJk9RNBJ4V3uPl6r9m7gq8BGghq68fvovgZ0mtlGgibYD5XrtYjMlHS+wJYBNaVOJmLGcc0xhrJOT1q1cSIiUylbVYC7v2mS4q8dYt1bgFsOsWwtcPok5WPA648mRpHZpqbUw1vYEGXncI5tQ1k6khEluiIih6EZG0Rm0eN9GZprpCnVolHMrKyPg45pxrKWOCM5p2ssX4FXLSJSO6r/SiIyR6TzBTYPZDh7fqomapg8n+fOXUNlPcaFi5sPKutMRmiKGduGcsxPRYnUwHslIlIJqokTmSWb+rNqSi2BmbG8JU467+wdVW2ciMihKIkTmSWP96VpjkVYWgNNqZXWnojQGo+wfShLXj1VRUQmpSROZBZk8s7mgQwnqldqSYJ742JkC7B7JDf1BiIidUhJnMgs2DiQIedwsuZKLVlbIsq8RISdQzlyBdXGiYhMpCROZBY83pumKWZqSp2mZS1xcg47h1UbJyIykZI4kTIbb0o9qT2pnpbT1ByP0JmKsmskRyav2jgRkWJK4kTKbFPYlHpSe6LSodSkZc0xCg47h7OVDkVEpKooiRMps8f7gqbU45rjlQ6lJjXGIixsiLJ7JE86r+m4RETGKYkTKaNsYbxXqppSj8ay5uBewq2DujdORGSckjiRMtoykCFbgBPb1JR6NJLRCIubYnSN5RnMqjZORASUxImU1ZP9GZLRYD5QOTpLm2LEI/DUQBbXAMAiIkriRMol787G/gwntCaIqin1qMUiwX2FA9kCPWnVxomIKIkTKZPtQ1nG8s6J6pU6Y45tiNIQM54azFJQbZyI1DklcSJl8mRfhpjBqlYlcTPFzFjVEmcs7xoAWETqnpI4kTJwd57sz7CqNUE8oqbUmdSejNKZjLBjKMeYhhwRkTqmJE6kDHaN5BjKFtSUWiYrW+NgQScHEZF6pSROpAye7MsQAU5QU2pZJKMRljbF6E4X6E3nKx2OiEhFKIkTmUHLlq/AzPiftY/wxN2/oCEexcxm/CGwpClGQ9TY1J8lX1AnBxGpP7FKByAyl2zftpXbtw7wYHea804/ibftGirLcS5c3FyW/daSiBkntMVZ15Nh61BWHUhEpO6oJk5khvWEzXudyWiFI5n7WhNRFjUG86oOZNSsKiL1RUmcyAzrHsvTEo+QiKrZczYsb46TjBob+rPkNXaciNQRJXEiM2je4mUM55zOlP60Zks0YpzQGowdt31IY8eJSP3QlUZkBp128csA6FBT6qxqT0ZZ2BBl53AwtIuISD1QEicyg0590ctpjBkNMf1pzbaVLXESEdjQn9GUXCJSF3SlEZkhw9kCy886j86UauEqIRYxVrUmGMk5OzQll4jUASVxIjNkQ3+GSCSiXqkV1JmKsiAVZcdQjkE1q4rIHKckTmSGPNmXpnv7Fhpj6pVaSata48QjsKEvo96qIjKnKYkTmQFj+QJPDWV55Bf/oxkVKiwWMVa3JRjNO9sGNbeqiMxdJSVxZnZ6uQMRqWWb+jMUHB6547ZKhyIEvVWPbYyyayRPv+ZWFZE5qtSauC+b2b1m9h4zay9nQCK16Mn+DE0xY/u6tZUORUIrmuOkwkGAk02apkxE5p6Skjh3fz7wZuA4YK2ZfcvMXlLWyERqRLbgbB7IcGJ7Etc9WFUjGjFWt8VJF5yX/+XfVzocEZEZV/I9ce6+Afgo8EHghcDnzexxM3ttuYITqQVbBjJkC3BimyZgrzatiShLm2Kc++o3s6E/XelwRERmVKn3xJ1pZp8FHgNeBPyhu58SPv9sGeMTqXpP9mdIRo1lLfFKhyKTOK45xq4n1nP7tiFGNOyIiMwhpdbEfQF4ADjL3d/r7g8AuPsugto5kbqUd2djf4YTWhNE1Su1KkXM+M7H3ks67/x4x5CavEVkzig1iXsZ8C13HwUws4iZNQK4+/WTbWBmXzezfWa2vqisw8x+amYbwp/zipZ92Mw2mtkTZvbSovJzzGxduOzzFo7fYGZJM/t2WH6Pma2Y9qsXOUrbh7KM5Z0T29WUWs32bHiUFyxq5Im+DI/2qllVROaGUpO4nwENRb83hmWHcy1w6YSyDwE/d/fVwM/D3zGzU4ErgNPCbb5oZuPD3n8JuBpYHT7G9/kOoNfdTyBo0v1Uia9FZMY82ZchZrCqVUlctTtvYQNLm2L8ZMcw/RkNOyIita/UJC7l7kPjv4TPGw+3gbvfCfRMKH4V8I3w+TeAVxeV3+juaXffAmwEzjOzRUCru9/tQRvIdRO2Gd/XzcAl47V0IrPB3XmyP8Oq1gTxiD561S5ixiuWt4DD97cMkiuoWVVEalupSdywmT17/BczOwcYPYLjHePuuwHCnwvD8iXA9qL1doRlS8LnE8sP2Mbdc0A/0DnZQc3sajNba2Zru7q6jiBskYPtHskxlC2oKbWGtCejvGx5M7tHctyxc7jS4YiIHJVYieu9H/iOme0Kf18EvHEG45isGsMPU364bQ4udL8GuAZgzZo1+votM+KJvgwR4AQ1pdaUk9qTnLsgy31dYyxtjnPqvGSlQxIROSIlJXHufp+ZnQycRJA8Pe7uRzIp4V4zW+Tuu8Om0n1h+Q6CgYTHLQV2heVLJykv3maHmcWANg5uvhUpi6ApNc3yljipmKYgrjUXLWli90iO27YO0hqPsLRZw8OISO2ZztXnXOBM4FnAm8zsyiM43q3AVeHzq4AfFJVfEfY4XUnQgeHesMl10MzOD+93u3LCNuP7uhy4wzV2gMyS/WN5etNqSq1VUTNeu7KVlkSEWzYP0DOmjg4iUntKHez3euBfgOcTJHPnAmum2OYG4G7gJDPbYWbvAP4ReImZbQBeEv6Ouz8C3AQ8CvwIeK+7j/9XfTfwVYLODpuA28PyrwGdZrYR+ABhT1eR2fBkfwaA1W1qiqtVjfEIbzi+DQxu2tTPkAYCFpEaU+o9cWuAU6dT0+XubzrEoksOsf4ngU9OUr4WOH2S8jHg9aXGIzKTnuxLs6QpRnNcTam1bF4yyuWrWrlxYz83buznTSe00aRzKiI1otT/VuuBY8sZiEi5LVu+AjM76kfHkuXsHc3zlb//yEHLpPYsaYrz+lVt9Gfy3LCxn2HVyIlIjSi1Jm4+8KiZ3Qs8Pdy5u7+yLFGJlMH2bVu5c9fQ1CtOYedwjqcGs3z0k/9Iw6f+6YBlFy5uPur9y+xb1hLn8lWtfGfTwNM1co2qkRORKldqEveJcgYhUkt6xvI0xowG9UqdU5a3JLj8+FZu3jTADUrkRKQGlPQfyt1/BTwFxMPn9wEPlDEukaqUyTsD2QKdqejUK0vNWREmcr3poGl1JKemVRGpXqX2Tv1Tgqmt/iMsWgJ8v0wxiVStnnTQabozqSRurlrRkuDyVUEid+PGfkaVyIlIlSq1reC9wAXAAIC7b+CZKbNE6kbPWJ5U1GiMqRPDXLaiNcHrVrXSPRbUyCmRE5FqVGoSl3b3zPgv4QwJGlhX6kqu4PRlCnQko+qJWgdWFiVyqpETkWpUahL3KzP7P0CDmb0E+A7ww/KFJVJ9+tJ5HOhM6Wb3erEqTOT2h4ncmBI5EakipV6NPgR0AeuAdwK3AR8tV1Ai1ag7XSAegRb1WKwrq1oTvHbleCI3oERORKpGqb1TC+7+FXd/vbtfHj5Xc6rUjYI7vek889SUWpeOb0vwmpWt7BvLccuWAfIF/fsTkcortXfqFjPbPPFR7uBEqkV/pkDe1Su1np3QluDly5rZPpTj9u1D6HusiFTadOZOHZcimLO0Y+bDEalOPWN5IgZtSTWl1rPTOlL0pgv8es8I85JRLji2sdIhiUgdK7U5tbvosdPd/xV4UXlDE6kO7k53Os+8RISomlLr3gXHNnDavCR37R7hkZ6xSocjInWspJo4M3t20a8Rgpq5lrJEJFJlhrJOtgAdmqVBADPjsmXNDGTz3LZtiNZElOOa45UOS0TqUKnNqZ8uep4jmILrDTMejUgV6k7nMaBD98NJKBYxXruyleuf7Oe7mwd464ntSvJFZNaVlMS5+8XlDkSkWvWM5WlNRIhF1JQqz2iIRXj98a1c92Qf397Uz1tObKMlrkRORGZPqc2pHzjccnf/zMyEI1JdRnIFRvPOosZSK62lnsxLRnnD8a3csGGAb28c4M2r22iIqfOLiMyOUv/brAHeTTDx/RLgXcCpBPfF6d44mbN6xoIJ7zs0S4McwqLGOK9b1UJvOs93NmkwYBGZPaVWL8wHnu3ugwBm9gngO+7+J+UKTKQadKfzNMeMZFRJnBza8pYEr1rRwvefGuRbG/t54/FtNGlmDxEps1L/yywDMkW/Z4AVMx6NSBVJ552hrOuGdSnJie1JLl/VSm86z39t6KMvna90SCIyx5WaxF0P3GtmnzCzjwP3ANeVLyyRyusJL8KapUFKtao1wRUntDGac77xRB+bBzJTbyQicoRKHez3k8DbgV6gD3i7u/9DGeMSqbiesTypqNEQU69UKd2SpjhXnthOczzCTZsGuGv3MAVN0SUiZTCdmzYagQF3/xyww8xWlikmkYrLFZz+TIHOlCa8l+nrSEW58qR2Tu9I8ps9o9ywsZ/+jJpXRWRmlZTEhU2oHwQ+HBbFgf8qV1AildabzuNAh+ZKlSMUjxgvX9bMy5c1s3ckz9cf7+Ox3nSlwxKROaTUK9RrgFcCwwDuvgsNLSJzWE+6QDwCLephKEfBzDijM8XbT26nMxnlB08N8t9bB0nnNQyJiBy9Uq9QGXd3wAHMrKl8IYlUVsGd3nSejqSaUmVmzEtGecuJbVxwbAOP9KT5+uN97BvNVTosEalxpSZxN5nZfwDtZvanwM+Ar5QvLJHK6c8UyLsmvJeZFTHjBYuaePPqNvIO39zQz46hbKXDEpEaNmUSZ0FVxLeBm4FbgJOAj7n7v5U5NpGK6B7LEzFoT6gpda6waFCrWs7HsuUrSoplaXOct6xuozFm3LixX8OQiMgRm3LGBnd3M/u+u58D/HQWYhKpGHenJ51nXjJKRE2pc4bn89y5a6isx7hwcXPJ67Yno7xldTs3bernu5sHeMuJ7Ryr+XlFZJpKrWr4nZmdW9ZIRKrAYLZAtgCd6pUqZdYUj/CG49tojEW4ZfMAQ1l1dhCR6Sn1SnUxQSK3ycweNrN1ZvZwOQMTqYSedAEjuBFdpNya4hFet6qVsXyB720ZIFfQoMAiUrrD1t+b2TJ33wZcNkvxiFSMu9M9lqctESEWUVOqzI5jGmO8fFkL339qkLt2j3DxEnX+F5HSTFUT930Ad98KfMbdtxY/yh6dyCwazTtjeU14L7Pv5HlJzuxMcu++UfaMaOgRESnNVElccXXEqnIGIlJpPWPBtEgdakqVCnjR4iYaY8bt2wY116qIlGSqJM4P8VxkzulO52mOG8momlJl9qViEV68tJm9o3nu2zda6XBEpAZMlcSdZWYDZjYInBk+HzCzQTMbOJIDmtlJZvZg0WPAzN5vZp8ws51F5S8r2ubDZrbRzJ4ws5cWlZ8TdrLYaGafNw2vL0conXeGsk6nauGkgk5uT3B8a5xf7xlhWL1VRWQKh03i3D3q7q3u3uLusfD5+O+tR3JAd3/C3c9297OBc4AR4Hvh4s+OL3P32wDM7FTgCuA04FLgi2Y2fqX9EnA1sDp8XHokMYn0pMOmVN0PJxVkZlyypJlcAX67d6TS4YhIlav0YFiXAJum6CTxKuBGd0+7+xZgI3CemS0CWt397nBe1+uAV5c9YpmTesbypKJGg5pSpcI6UlHO7Ezy+/1j9IVfLkREJlPpJO4K4Iai398XjkP3dTObF5YtAbYXrbMjLFsSPp9YLjItuYLTnylownupGhcc24gBv96j2jgRObSKJXFmlgBeCXwnLPoScDxwNrAb+PT4qpNs7ocpn+xYV5vZWjNb29XVdTRhyxzUlyngQEeq0t9pRAKtiSjPnp/ikZ40+0c15IiITK6SV63LgAfcfS+Au+9197y7F4CvAOeF6+0AjivabimwKyxfOkn5Qdz9Gndf4+5rFixYMMMvQ2pdz1iemEFrXEmcVI/nHttIPGL8dq96qorI5Cp51XoTRU2p4T1u414DrA+f3wpcYWZJM1tJ0IHhXnffDQya2flhr9QrgR/MTugyV7g7veGE92pKlWrSGItw9vwUj/WmdW+ciEyqIkmcmTUCLwG+W1T8T0Vzsl4M/AWAuz8C3AQ8CvwIeK+7j/9HezfwVYLODpuA22fnFchcMZgtkHP1SpXqdO6CFGZwr8aNE5FJHHbu1HJx9xGgc0LZWw+z/ieBT05SvhY4fcYDlLoxPuF9e0JNqVJ9WhJRTp+X5OHuMS44tpEmNfmLSBH9R5C61jOWp1UT3ksVe84xDeQc7u9SbZyIHEhJnNSt0VyB0bxrrlSZERYN7quc6cf8hjjr7/hvfr5hN6tOOqXSL1NEqkhFmlNFqsHTszQk9V1Gjp7n89y5a6gs+x7I5FnXk+GYs55blv2LSG3S1UvqVk+6QGPMSMX0ZyDVrSUeoTlmPPeKPyGYoEZEREmc1KlcwRkIZ2kQqXZmxqKmGAtXnsiWwWylwxGRKqEkTupS79NNqUripDbMT0UZ3L9PHRxE5GlK4qQu9aQLxCPQHFevVKkNETPuufk/2TSQpWdMg/+KiJI4qUMFzdIgNeqem79B1OD+/aqNExElcVKHBjIF8q6mVKk9Qz1dnNSeZH1PmmxBHRxE6p2SOKk7Pem8ZmmQmnX2/BTpvPNYb7rSoYhIhekqJnUlmPC+QHsyQlSzNEgNOq4pRmcqyoP7xyodiohUmJI4qSujeWcs78xTU6rUKDPj7M4Uu0Zy7B3JVTocEakgJXFSV8Z79el+OKllZ3QkiRk82K3aOJF6piRO6kpPukBTzEhG1ZQqtSsVi3DyvCSP9KTJ5NXBQaReKYmTutHU3slgtkBHSrVwUvvO7kyRKTiP96mDg0i9UhIndeOk578YUFOqzA1LmmLMS0ZY16MmVZF6pSRO6sYpL7yURASaYmpKldpnZpzRkWL7UI6+tGZwEKlHSuKkLuQKzurzL6JDszTIHHJaRxKA9T1qUhWpR0ripC5sG8qSbGpmnu6HkzmkLRFlRUucdT1juKuDg0i9URIndWFDf4b0yLBmaZA55/SOJP2ZAtuHNWacSL3RFU3mPHdnY3+GDXf/goiaUmWOObEtSSJirNOYcSJ1R0mczHl7RnMMZgs8+svbKx2KyFGxaHBPZ/EjGYvw2+9/i3u37iOWSB60/Egey5avqPRLFZESxCodgEi5bejLYMATv/lZpUMROSqez3PnrqGDynvTeR7tzXDzE110zsB9nxcubj7qfYhI+akmTua8Df0ZljbHGOnrqXQoImXRlogQM9g/pqFGROqJkjiZ0/rSebrG8qxuS1Y6FJGyiZgxPxWlJ50nX1AvVZF6oSRO5rQN/RkAVrclKhyJSHnNb4hScOjRwL8idUNJnMxpG/ozzE9FmaeptmSOa41HSETUpCpST5TEyZw1miuwfSirWjipCxY2qfamC+TUpCpSF5TEyZy1aSCDo6ZUqR/zUzEcNamK1AslcTJnbejP0ByLsKhRI+lIfWiOG8mIqUlVpE4oiZM5KVdwNg9kOKEtoQnvpW6YGZ2pKH1qUhWpC0riZE7aOpglW1BTqtSfzlRETaoidUJJnMxJG/ozxCOwvCVe6VBEZlVLPEIiYnSrSVVkzlMSJ3PO+IT3q1oTxCJqSpX6EjSpRtRLVaQOKImTOWf3SI6hXEFNqVK35qeialIVqQNK4mTOebI/mPD++FYlcVKfWsKBf9WkKjK3VSSJM7OnzGydmT1oZmvDsg4z+6mZbQh/zita/8NmttHMnjCzlxaVnxPuZ6OZfd7UDbHuuTuP96ZZ0RKnIabvKFKfxnupqklVZG6r5FXuYnc/293XhL9/CPi5u68Gfh7+jpmdClwBnAZcCnzRzMbnUPoScDWwOnxcOovxSxXaO5qnL1Pg5HZNeC/1rTNsUu1Vk6rInFVNVRWvAr4RPv8G8Oqi8hvdPe3uW4CNwHlmtghodfe73d2B64q2kTr1eG8aA05sV1Oq1LfWeIS4mlRF5rRKJXEO/MTM7jezq8OyY9x9N0D4c2FYvgTYXrTtjrBsSfh8YvlBzOxqM1trZmu7urpm8GVINXF3Hu9TU6oIHNikmleTqsicVKkr3QXu/mzgMuC9ZnbhYdad7D43P0z5wYXu17j7Gndfs2DBgulHKzXh6abUeWpKFYGgl2oBNamKzFUVSeLcfVf4cx/wPeA8YG/YREr4c1+4+g7guKLNlwK7wvKlk5RLnXq8N00EOFFDi4gAzzSpai5Vkblp1pM4M2sys5bx58AfAOuBW4GrwtWuAn4QPr8VuMLMkma2kqADw71hk+ugmZ0f9kq9smgbqTPjTanL1ZQq8jQzozOpJlWRuSpWgWMeA3wvHA0kBnzL3X9kZvcBN5nZO4BtwOsB3P0RM7sJeBTIAe919/Gvle8GrgUagNvDh9Sh3SM5+jIFnndsY6VDEakqnakoe0bz9GYKzE9Fp95ARGrGrCdx7r4ZOGuS8m7gkkNs80ngk5OUrwVOn+kYpfY80psmauqVKjJRW+KZJlUlcSJzi9qdpObl3XmsN80JbQlSUX2kRYqZGR3JKL3pPHlXk6rIXKIrntS8pwayjOSc09QrVWRS81NRCg596UKlQxGRGaQkTmreo71pUlHTXKkih9CWiBAz9VIVmWuUxElNy+SdJ/vTnNyeJBrR1Lkikxkf+LdHTaoic4qSOKlpG/rTZAtwWoeaUkUOR02qInOPkjipaet70rTGIyxtqsRoOSK1o1VNqiJzjpI4qVmDmTxPDWY5vTNJOO6giBxCRE2qInOOkjipWet70jhwRkeq0qGI1IQFDUGTao9q40TmBCVxUpPcnfU9aZY2xZiX1ACmIqVojUdIRox9o0riROYCJXFSk3aP5OhO5zmjU7VwIqUyMxY0ROnLFEjn1aQqUuuUxElNWteTJmZwsqbZEpmWhQ1BzfX+sVyFIxGRo6UkTmpOruA82pvmpPYkSU2zJTItDbEILfGgSdXVwUGkpukKKDXnib406bxzRqfGhhM5EgsaYozknOGckjiRWqYkTmrOQ91p2hMRljfHKx2KSE2an4piwL5RNamK1DIlcVJTesbybBvKcmZnSmPDiRyheCQYM27fqMaME6llSuKkpjzcPYaBmlJFjtIxDVHyDt0aM06kZimJk5qRd2ddzxjHtyVoiWtsOJGj0ZaIkIoae0eUxInUKiVxUjM29mcYzjlna2w4kaNmZhzTEGUgW2AkV6h0OCJyBJTESc14qHuMlniEVa3q0CAyExY2xDBQbZxIjVISJzWhP5Nn80CWMzqSRNShQWRGJKLGvGSEfaM5CurgIFJzlMRJTXi4ewyAM9WUKjKjFjXGyDnsVwcHkZqjJE6qXsGddd1pVrTEaddk9yIzqi0RoSFq7BrOaQYHkRqjJE6q3paBLAPZgjo0iJSBmbGoKcZwzhnMqoODSC1REidV76HuMRpixuo2TXYvUg4LU1GiBrvVwUGkpiiJk6qwbPkKzOygR8v8Y3hs/zA//voXiEUjk65T6kNEJheNGMc0xNg/liedV5OqSK2IVToAEYDt27Zy566hg8q3DWXZPpTjf33gA3z4r//qqI5x4eLmo9peZC5b1Bhl10iOPSOaT1WkVqgmTqpWwZ09IznaExEaY/qoipRTKhahIxlhz0iOWELT2onUAl0ZpWp1j+XJFoIhEESk/MaHGznzpa+pdCgiUgIlcVK1do/kSYWDkYpI+bUlIjTGjOdd8acabkSkBujqKFVpKFtgMFtgUWNMnRJEZomZsagxxpJTzmTHsO6NE6l2SuKkKu0ezhExWNigwX1FZtOChigj/b3c3zVa6VBEZApK4qTqpPNO11ieYxqixCKqhROZTVEz7vve9TzRl6E/o3HjRKqZkjipOruGcziwWB0aRCritzd+FQPu3afaOJFqpiROqkq24OwZzbEgFSWlYUVEKmJg325O7UjycPcYozlNxSVSrXSVlKqyZyRHwWFJk2rhRCrpOQsbyBbg/q6xSociIocw60mcmR1nZr8ws8fM7BEz+19h+SfMbKeZPRg+Xla0zYfNbKOZPWFmLy0qP8fM1oXLPm/qxljT8u7sGs4xLxmhKa7vFyKVtKAhxvGtce7fP0q2oOFGRKpRJa6UOeAv3f0U4HzgvWZ2arjss+5+dvi4DSBcdgVwGnAp8EUzG++y+CXgamB1+Lh0Fl+HzLC9I3lyqoUTqRrnH9PIaM55uFu1cSLVaNaTOHff7e4PhM8HgceAJYfZ5FXAje6edvctwEbgPDNbBLS6+90ejEp5HfDq8kYv5RJPNbJjOEtbIkJbQsOKiFSDpU0xljTFuGfvKHnVxolUnYq2WZnZCuBZwD1h0fvM7GEz+7qZzQvLlgDbizbbEZYtCZ9PLJ/sOFeb2VozW9vV1TWTL0FmyPlveDvZAixrVi2cSLUwMy44tpGBbIF1PelKhyMiE1QsiTOzZuAW4P3uPkDQNHo8cDawG/j0+KqTbO6HKT+40P0ad1/j7msWLFhwtKHLDEvnC7zwqj+jPRGhVbVwIlVlZUucRY0x7t47Ql5TcYlUlYokcWYWJ0jgvunu3wVw973unnf3AvAV4Lxw9R3AcUWbLwV2heVLJymXGnN/1xhN8zpZ1hyvdCgiMsF4bVx/psB61caJVJVK9E414GvAY+7+maLyRUWrvQZYHz6/FbjCzJJmtpKgA8O97r4bGDSz88N9Xgn8YFZehMyY0VyBe/aN8tidP6YloR6pItXo+NY4xzbEuHuPauNEqkklrpoXAG8FXjRhOJF/CocLeRi4GPgLAHd/BLgJeBT4EfBedx+fC+bdwFcJOjtsAm6f3ZciR+s3e0bI5J0fff7vKh2KiByCmXHBogb6MgXWdas2TqRazPpd5O7+aya/n+22w2zzSeCTk5SvBU6fuehkNvWm8zywf4wzO5Ps2/xEpcMRkcM4oTXBkqYYv94zwmkdSeKa11ik4tR+JRXzy13DRA1esKip0qGIyBTMjBcubmIoW+D+Ls2pKlINlMRJRewYyvJEX4bnLGykWbMziNSEZc1xjm+Nc/feUcY0p6pIxenqKbPO3fnFrmGaYxHOW9hQ6XBEZBpeuLiJdN65e69q40QqTUmczLon+jLsHM7xgsWNJKK6r0aklixsiHF6R5L7ukbpGctPvYGIlI2SOJlV+YLzy13DLEhFOaMjWelwROQIXLS4ibgZP9sxhGvIEZGKURIns+qB/WP0ZQpcvKSJiKkWTqQWNccjPH9RI5sHs2zoz1Q6HJG6pSROZs1orsBv9oywoiXOqtZEpcMRkaPw7AUp5qei/HznMNmCauNEKkFJnMyaX+0aIZ13LlmiIUVEal3UjJcsbaI/U+Cu3SOVDkekLimJk1mxZyTHg91jrFmQYkHDrI8xLSJlsLwlwbPmp7h33yjbh7KVDkek7iiJk7Jzd36yfYimmHHBosZKhyMiM+jixU20JyL8z9ZBMnk1q4rMJiVxUnYP96TZNZLjosVNpKL6yInMJYmo8bLlLfRlCtyxc7jS4YjUFV1RpawGs3nu2DnM0qZgbCkRmXuWNcd5zsIGHuweY133WKXDEakbSuKkbNydH28fJl9wXrasBdOQIiI1waJRzGxajxcvb2PTfXfx/Q37WXrq2VOuv2z5ikq/TJGapzvMpWwe68uwsT/DxYsb6UhFKx2OiJTI83nu3DU07e2yBeeh/Wk+cMPPOaszedgZWS5c3Hw0IYoIqomTKSxbvmLa38jNjLaFi/jW77ewff0DPG9J65Tri0jti0eMk+clyBWcR3rTGj9OpMxUEyeHtX3b1ml/Iy+4s74nw3CuwAtWL+GK7f1TbqNv5SJzQ3M8winzEjzam+GRnjSndySJRfRFTaQcVBMnM27rYI7BbIETWuM0xvQRE6k37ckoJ7cnGMkFNXIaekSkPHSFlRm1fzTHrpEcxzZGNaivSB3rSEU5qT3BSNZ5qDvNULZQ6ZBE5hwlcTJjetN5nuzP0hKPsLIlXulwRKTCOlNRzugMhhZa15Nm32gOd9XKicwUJXEyIwYyeR7vzdAYM06dlyCizgoiQnCP3FmdSZpjETb0Z3msL0NazasiM0JJnBy1vnSeR3szJKPGqfN0E7OIHCgRNU7vSLCyJU5/usDv949x0R+/X71XRY6Skjg5KrtHcjwSJnCndRx+XCgRqV9mxuKmGGfPT9KWiPDS932Eax7t5YGuUSVzIkdISZwckbw7m/ozbB7IMi8Z4YyOJEklcCIyhYZYhFPmJfnyH7+C1kSEn+wY5ovre7hz1zDD6vwgMi3qPijTNpgp8GR/hrG8s7gxxoqWmAbsFZFp2frgPbxldRs7h3Pcs2+U3+4d5Z59o5zWkeTcBQ3q3S5SAv2VSMlyBWfbUJbdI3kSEeO0eQnak5pOS0SOjJmxtDnO0uY4PWN57usaZV33GA93pzmuOcY58xtY3Z4gqi+JIpNSEidTcne6xvI8NZglW4BjG6Isb4mrA4OIHDGLRietwW9s72DNq/6I51z+NrYPLWegaw/3fvc67r3lOgb37532cY5btpxtW5+agYhFqo+SODkkd+fE572IB7vTjOSc5rhxyrwELXHdSikiR8fz+cNO6efu9KYL7EkspvWdf81L3vnXdKSiLGqM0hqPlHwLh6b0k7lMSZxMas9Ijl/sHObtX/g2eYcT2+LMT03+zVlEZKaZGR2pKB2pKKO5AntG8uwbzdE9lqcxZhzbGGNBKqoWAalrqlKRA+wfzfGDLQNc+0Qf+0Zz/PCfP8Kz5ydZ0KDOCyJSGQ2xCCtb46xZmOKE1jgGbB7IsrZrjM0DGUZz6tUq9Uk1cQLAvtEcv90zwuN9GeIReO4xDTznmAbef8M1RD79mUqHJyJC1IxjGmMsbIgylHV2j+TYM5Jn90ie9kSExU0x2hOlN7WK1DolcXVuz0iQvD3ZnyERMZ57TAPnLmygMaZKWhGpTmZGS8JoSSRYkXf2jObYM5Lj0d4MqagxPxVlfipKk+7flTlOSVwdyruzoS/D2q5RdgznSEaM5x3bwLkLGmhQ8iYiNSQRNZY1x1naFKN7LM++0Tw7hnPB/7ao8ZqPfoZHesZY2BCjIxXVcCUypyiJqxPuzs7hHI/2pnmsL81ozmlLRLh4cSNndaZIKXkTkRoWMWNBQ4wFDTEyeac7nacvneeMl7ySH24dCteBlniEpliEpvBnY8xoiEVIRY1UzEhFIySjFvwejRCPoOZZqVpK4uawgUye7UNZtgxm2TyQYSTnxAxWtyU4rSPFqtY4Ef1zEpE5JhE1FjXGWNQY411nnMieoTRdozm6RvMMZAuM5Ar0pfPsHM4ymnMON3NrxKA1HqE9GaU9EaU9GaE9EWVeMug5G1fvWKkgJXFzgLvTnymwbzTHvtGgG/6e0RwDmaDHVipqrGpNcHxrnBPaEiSjqnUTkTphxjGN8UMvjkRoaGkj1dL2zM/WdhpaWmlobaexbR7txy6lY8ly5i1dTvO8+U9vWygU6N25lcE9O3jDy/+AzvBevM5kVK0bMitqPokzs0uBzwFR4Kvu/o8VDmnGFdwZyzkjuQJDuQL96QK9mTz/dcsPSHUsZP5xq0i1tAbrFgp0b9vM7icfYetD97LlgbvZs+ERvKAu+CJSf6YaVHi6cgUnnQ/+H4/mnAWrT2D92Aj3d42SL6rSa4oZnakY7ckILfEILfEoLfEIzeGjIWZqCZGjVtNJnJlFgX8HXgLsAO4zs1vd/dHKRnZ4+YIzmndGc0G1/kiYoI3/Uxguej7+c2J1f8Sg/bjjOWH1alIxC+7xCO/viC4+A84/A7jiqGPVaOciIs+IRYxYxA7o+frOP7oE3OlYspwFK1ezcOWJLFh5IgtXrmbeomU0dcwnEjm4Zm6kv5fh3m6G+3oY7t3PSF9P+Hs3w73djA70kh4eJj0yREdbK3ff9SvikWColYihJFBqO4kDzgM2uvtmADO7EXgVULEkrjed58q/+hjpgtPYNq/o0UFDWzuNbR0kG5sOuf1IXw9Dvd0M9+4P/pif/tnNUE8XQz1d9OzcRv/enXihMKPfMEVEZPqmqu0ruJMtOJk8pAvB82zByTbOJ3fM/Gd+Lzi5Aoe8R+8L63sOKosaTyd00fBnxIJyI3huBOuYGZGnnz+zPEL4e7jcwrKIWbjeM8ewSZY/vU24fPyYZgfuP0K4/tP7nLD9ZMunOPZ4mRE8CF8X478fUM4B5QeWhdvYM+scartq6uhS60ncEmB70e87gOdUKBYA9o/luOCP/wKAmEE8/NYWi0Dcgp/j3+TGlz/zADt2CcHLmppqyUREql/EjGTUSEahZYp13Z28EyR0HrTc5B3+9s//lESqkURDI5FYnGgsRjQWn/A8+Dn+PBKNYpEokWj4iESxaJRIJEIkGnvmeSRKJBbDIlGi0SiMl4XbTb6PKBaJBMcJl9ejixc38pxjGit2fHM/XL+c6mZmrwde6u5/Ev7+VuA8d/+zCetdDVwd/noS8ESZQ5sP7C/zMWT6dF6qj85JddJ5qT46J9VpNs7LcndfMNmCWq+J2wEcV/T7UmDXxJXc/RrgmtkKyszWuvua2TqelEbnpfronFQnnZfqo3NSnSp9Xmq9D/R9wGozW2lmCYI7+W+tcEwiIiIiZVfTNXHunjOz9wE/Jhhi5Ovu/kiFwxIREREpu5pO4gDc/TbgtkrHMcGsNd3KtOi8VB+dk+qk81J9dE6qU0XPS013bBARERGpV7V+T5yIiIhIXVISdxTM7FIze8LMNprZhyZZbmb2+XD5w2b27ErEWW9KOC9vDs/Hw2b2WzM7qxJx1pOpzknReueaWd7MLp/N+OpRKefEzC4yswfN7BEz+9Vsx1iPSvj/1WZmPzSzh8Lz8vZKxFlPzOzrZrbPzNYfYnnFrvVK4o5Q0ZRflwGnAm8ys1MnrHYZsDp8XA18aVaDrEMlnpctwAvd/Uzg79C9JmVV4jkZX+9TBB2VpIxKOSdm1g58EXilu58GvH6246w3Jf6tvBd41N3PAi4CPh2OziDlcy1w6WGWV+xaryTuyD095Ze7Z4DxKb+KvQq4zgO/A9rNbNFsB1pnpjwv7v5bd+8Nf/0dwfiCUj6l/K0A/BlwC7BvNoOrU6Wckz8Cvuvu2wDcXeel/Eo5Lw60WDD3UzPQA+RmN8z64u53ErzPh1Kxa72SuCM32ZRfE+fLKmUdmVnTfc/fAdxe1ohkynNiZkuA1wBfnsW46lkpfycnAvPM7Jdmdr+ZXTlr0dWvUs7LF4BTCAa2Xwf8L3cvzE54cggVu9bX/BAjFTTZDLgTu/qWso7MrJLfczO7mCCJe35ZI5JSzsm/Ah9093w1TS49h5VyTmLAOcAlQANwt5n9zt2fLHdwdayU8/JS4EHgRcDxwE/N7C53HyhzbHJoFbvWK4k7cqVM+VXStGAyo0p6z83sTOCrwGXu3j1LsdWrUs7JGuDGMIGbD7zMzHLu/v1ZibD+lPr/a7+7DwPDZnYncBagJK58Sjkvbwf+0YPxwTaa2RbgZODe2QlRJlGxa72aU49cKVN+3QpcGfZcOR/od/fdsx1onZnyvJjZMuC7wFtVqzArpjwn7r7S3Ve4+wrgZuA9SuDKqpT/Xz8AXmBmMTNrBJ4DPDbLcdabUs7LNoLaUczsGOAkYPOsRikTVexar5q4I3SoKb/M7F3h8i8TzCTxMmAjMELwDUrKqMTz8jGgE/hiWPOT08TS5VPiOZFZVMo5cffHzOxHwMNAAfiqu086xILMjBL/Vv4OuNbM1hE0433Q3fdXLOg6YGY3EPQEnm9mO4CPA3Go/LVeMzaIiIiI1CA1p4qIiIjUICVxIiIiIjVISZyIiIhIDVISJyIiIlKDlMSJiIiI1CAlcSIy55mZm9n1Rb/HzKzLzP67DMd61/gUVWb2NjNbfAT7eMrM5s90bCIyt2icOBGpB8PA6WbW4O6jwEuAneU40IRx794GrEcztYhIGagmTkTqxe3Ay8PnbwJuGF9gZueZ2W/N7Pfhz5PC8kYzu8nMHjazb5vZPWa2Jlw2ZGafNLOHzOx34ej5mNknzOyvzOxygunEvmlmD5pZQ3ENm5mtMbNfhs87zewn4fH/g6K5GM3sLWZ2b7iP/zCzaNnfKRGpCUriRKRe3AhcYWYp4EzgnqJljwMXuvuzCGb0+Iew/D1Ar7ufSTBS/jlF2zQBv3P3s4A7gT8tPpi73wysBd7s7meHNYCH8nHg1+HxbwWWAZjZKcAbgQvc/WwgD7x5ui9cROYmNaeKSF1w94fNbAVBLdxtExa3Ad8ws9WAE06pAzwf+Fy4/Xoze7homwwwfk/d/QRNtEfqQuC14XH+x8x6w/JLCBLH+8Ip4hqAfUdxHBGZQ5TEiUg9uRX4F4J5EDuLyv8O+IW7vyZM9H4ZlhuHlvVn5i3MU9r/0xzPtICkJiybbA5EA77h7h8uYd8iUmfUnCoi9eTrwN+6+7oJ5W0809HhbUXlvwbeAGBmpwJnTPN4g0BL0e9P8UyT7OuKyu8kbCY1s8uAeWH5z4HLzWxhuKzDzJZPMwYRmaOUxIlI3XD3He7+uUkW/RPw/8zsN0Bxx4EvAgvCZtQPAg8D/dM45LXAl8c7NgB/A3zOzO4iqL0b9zfAhWb2APAHwLYw3keBjwI/CWP4KbBoGscXkTnMnmkNEBGRYmFP0Li7j5nZ8QQ1Yye6e6bCoYmI6J44EZHDaAR+YWZxgvvT3q0ETkSqhWriRERERGqQ7okTERERqUFK4kRERERqkJI4ERERkRqkJE5ERESkBimJExEREalBSuJEREREatD/BxN2Np3Zr6rUAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T13:00:36.209997Z",
     "start_time": "2024-11-19T13:00:35.804194Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Plot the distribution of Magnitude values in the JMA dataset\n",
    "plt.figure(figsize=(10, 6))\n",
    "seaborn.histplot(jma_df['Magnitude'], bins=20, color='salmon', kde=True)\n",
    "plt.title('Distribution of Earthquake Magnitudes (JMA)')\n",
    "plt.xlabel('Magnitude')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ],
   "id": "9bf54cb608eb77e6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-19T13:01:14.425313Z",
     "start_time": "2024-11-19T13:01:14.000926Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# save the distribution of magnitude pictures\n",
    "plt.figure(figsize=(10, 6))\n",
    "seaborn.histplot(bmkg_df['Magnitude'], bins=20, color='skyblue', kde=True)\n",
    "plt.title('Distribution of Earthquake Magnitudes (BMKG)')\n",
    "plt.xlabel('Magnitude')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('bmkg_magnitude_distribution.png')"
   ],
   "id": "9d016028350de50a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# the full sammary of the data preparation and preprocessing\n",
    "\n",
    "# 1. Load and inspect the datasets\n",
    "# 2. Data Cleaning\n",
    "# 3. Date-Time Formatting\n",
    "# 4. Standardize Column Names\n",
    "# 5. Convert Coordinates to Decimal Degrees\n",
    "# 6. Normalization and Scaling\n",
    "# 7. Train-Test Split\n",
    "# 8. Save the cleaned and preprocessed\n",
    "# 9. Visualize the data\n"
   ],
   "id": "2f3b54e4c8183bea"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "d9752669dc9f3acb"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
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